library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(psych)
library(whitening)
library("vioplot")
library("rpart")
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Data Source https://archive.ics.uci.edu/ml/datasets/seeds
M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, ‘A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images’, in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 15-24.
seeds <- read.delim("~/GitHub/LatentBiomarkers/Data/seeds_dataset.txt", header=FALSE)
par(cex=0.5)
featnames <- c("area",
"perimeter",
"compactness",
"length_of_kernel",
"width_of_kernel",
"asymmetry_coeff",
"length_ker_groove",
"class"
)
colnames(seeds) <- featnames
seeds$class <- 1*(seeds$class == 1)
pander::pander(table(seeds$class))
| 0 | 1 |
|---|---|
| 140 | 70 |
studyName <- "Seeds"
dataframe <- seeds
outcome <- "class"
thro <- 0.80
TopVariables <- 5
cexheat = 0.45
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 210 | 7 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 140 | 70 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) > 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9943409
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 7 , Uni p: 0.03711537 , Uncorrelated Base: 2 , Outcome-Driven Size: 0 , Base Size: 2
#>
#>
1 <R=0.994,r=0.972,N= 3>, Top: 1( 1 )[ 1 : 1 Fa= 1 : 0.972 ]( 1 , 1 , 0 ),<|>Tot Used: 2 , Added: 1 , Zero Std: 0 , Max Cor: 0.971
#>
2 <R=0.971,r=0.935,N= 3>, Top: 1( 2 )[ 1 : 1 Fa= 1 : 0.935 ]( 1 , 2 , 1 ),<|>Tot Used: 4 , Added: 2 , Zero Std: 0 , Max Cor: 0.864
#>
3 <R=0.864,r=0.832,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 2 : 0.832 ]( 2 , 2 , 1 ),<|>Tot Used: 5 , Added: 2 , Zero Std: 0 , Max Cor: 0.825
#>
4 <R=0.825,r=0.800,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 2 : 0.800 ]( 1 , 1 , 2 ),<|>Tot Used: 5 , Added: 1 , Zero Std: 0 , Max Cor: 0.714
#>
5 <R=0.714,r=0.800,N= 0>
#>
[ 5 ], 0.7135263 Decor Dimension: 5 Nused: 5 . Cor to Base: 4 , ABase: 1 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
13
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
10.8
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
4.55
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
3.73
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.7135263
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| asymmetry_coeff | 2.67 | 1.1739 | 4.217 | 1.3818 | 9.80e-01 | 0.810 |
| length_ker_groove | 5.09 | 0.2637 | 5.569 | 0.5009 | 1.98e-03 | 0.764 |
| compactness | 0.88 | 0.0162 | 0.866 | 0.0254 | 5.69e-01 | 0.653 |
| length_of_kernel | 5.51 | 0.2315 | 5.689 | 0.5075 | 3.68e-04 | 0.562 |
| perimeter | 14.29 | 0.5766 | 14.692 | 1.5318 | 2.01e-05 | 0.524 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]
pander::pander(univarDe$orderframe[topLAvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| La_length_ker_groove | 3.00 | 0.1832 | 3.365 | 0.1729 | 0.849 | 0.927 |
| asymmetry_coeff | 2.67 | 1.1739 | 4.217 | 1.3818 | 0.980 | 0.810 |
| La_width_of_kernel | 3.29 | 0.0455 | 3.255 | 0.0493 | 0.939 | 0.712 |
| compactness | 0.88 | 0.0162 | 0.866 | 0.0254 | 0.569 | 0.653 |
| La_length_of_kernel | 3.43 | 0.1286 | 3.504 | 0.1377 | 0.993 | 0.637 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))
theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.5 | 4 | 0.571 |
allSigvars <- names(dc)
dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
coef <- theFormulas[[dx]]
cname <- names(theFormulas[[dx]])
names(cname) <- cname
for (cf in names(coef))
{
if (cf != dx)
{
if (coef[cf]>0)
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
}
else
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("%5.3f*%s",coef[cf],cname[cf]))
}
}
}
}
finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| La_length_ker_groove | -0.146area + 1.000length_ker_groove | 3.00 | 0.1832 | 3.365 | 0.1729 | 8.49e-01 | 0.927 | 0.764 | -1 |
| asymmetry_coeff | 2.67 | 1.1739 | 4.217 | 1.3818 | 9.80e-01 | 0.810 | 0.810 | NA | |
| length_ker_groove | NA | 5.09 | 0.2637 | 5.569 | 0.5009 | 1.98e-03 | 0.764 | 0.764 | NA |
| La_width_of_kernel | -0.204area + 0.540length_of_kernel + 1.000*width_of_kernel | 3.29 | 0.0455 | 3.255 | 0.0493 | 9.39e-01 | 0.712 | 0.501 | -2 |
| compactness | 0.88 | 0.0162 | 0.866 | 0.0254 | 5.69e-01 | 0.653 | 0.653 | NA | |
| La_length_of_kernel | -0.145area + 1.000length_of_kernel | 3.43 | 0.1286 | 3.504 | 0.1377 | 9.93e-01 | 0.637 | 0.562 | 1 |
| length_of_kernel | NA | 5.51 | 0.2315 | 5.689 | 0.5075 | 3.68e-04 | 0.562 | 0.562 | NA |
| area | NA | 14.33 | 1.2157 | 15.104 | 3.4347 | 1.39e-05 | 0.515 | 0.515 | 4 |
| width_of_kernel | NA | 3.24 | 0.1776 | 3.266 | 0.4458 | 3.59e-03 | 0.501 | 0.501 | NA |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,tol=0.002) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 134 | 6 |
| 1 | 21 | 49 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.2619 | 0.2038 | 0.3269 |
| tp | 0.3333 | 0.2700 | 0.4015 |
| se | 0.7000 | 0.5787 | 0.8038 |
| sp | 0.9571 | 0.9091 | 0.9841 |
| diag.ac | 0.8714 | 0.8185 | 0.9135 |
| diag.or | 52.1111 | 19.8637 | 136.7104 |
| nndx | 1.5217 | 1.2692 | 2.0504 |
| youden | 0.6571 | 0.4877 | 0.7879 |
| pv.pos | 0.8909 | 0.7775 | 0.9589 |
| pv.neg | 0.8645 | 0.8004 | 0.9141 |
| lr.pos | 16.3333 | 7.3559 | 36.2669 |
| lr.neg | 0.3134 | 0.2188 | 0.4491 |
| p.rout | 0.7381 | 0.6731 | 0.7962 |
| p.rin | 0.2619 | 0.2038 | 0.3269 |
| p.tpdn | 0.0429 | 0.0159 | 0.0909 |
| p.tndp | 0.3000 | 0.1962 | 0.4213 |
| p.dntp | 0.1091 | 0.0411 | 0.2225 |
| p.dptn | 0.1355 | 0.0859 | 0.1996 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 49 | 6 | 55 |
| Test - | 21 | 134 | 155 |
| Total | 70 | 140 | 210 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.871 | 0.818 | 0.914 |
| 3 | se | 0.700 | 0.579 | 0.804 |
| 4 | sp | 0.957 | 0.909 | 0.984 |
| 6 | diag.or | 52.111 | 19.864 | 136.710 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 130 | 10 |
| 1 | 7 | 63 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.3476 | 0.2834 | 0.416 |
| tp | 0.3333 | 0.2700 | 0.401 |
| se | 0.9000 | 0.8048 | 0.959 |
| sp | 0.9286 | 0.8726 | 0.965 |
| diag.ac | 0.9190 | 0.8736 | 0.952 |
| diag.or | 117.0000 | 42.5430 | 321.768 |
| nndx | 1.2069 | 1.0822 | 1.476 |
| youden | 0.8286 | 0.6773 | 0.924 |
| pv.pos | 0.8630 | 0.7625 | 0.932 |
| pv.neg | 0.9489 | 0.8976 | 0.979 |
| lr.pos | 12.6000 | 6.8989 | 23.012 |
| lr.neg | 0.1077 | 0.0532 | 0.218 |
| p.rout | 0.6524 | 0.5838 | 0.717 |
| p.rin | 0.3476 | 0.2834 | 0.416 |
| p.tpdn | 0.0714 | 0.0348 | 0.127 |
| p.tndp | 0.1000 | 0.0412 | 0.195 |
| p.dntp | 0.1370 | 0.0677 | 0.238 |
| p.dptn | 0.0511 | 0.0208 | 0.102 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 63 | 10 | 73 |
| Test - | 7 | 130 | 137 |
| Total | 70 | 140 | 210 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.919 | 0.874 | 0.952 |
| 3 | se | 0.900 | 0.805 | 0.959 |
| 4 | sp | 0.929 | 0.873 | 0.965 |
| 6 | diag.or | 117.000 | 42.543 | 321.768 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 138 | 2 |
| 1 | 23 | 47 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.2333 | 0.17789 | 0.2965 |
| tp | 0.3333 | 0.26996 | 0.4015 |
| se | 0.6714 | 0.54878 | 0.7791 |
| sp | 0.9857 | 0.94935 | 0.9983 |
| diag.ac | 0.8810 | 0.82929 | 0.9215 |
| diag.or | 141.0000 | 32.02203 | 620.8538 |
| nndx | 1.5217 | 1.28648 | 2.0075 |
| youden | 0.6571 | 0.49813 | 0.7773 |
| pv.pos | 0.9592 | 0.86021 | 0.9950 |
| pv.neg | 0.8571 | 0.79339 | 0.9072 |
| lr.pos | 47.0000 | 11.75707 | 187.8870 |
| lr.neg | 0.3333 | 0.23833 | 0.4662 |
| p.rout | 0.7667 | 0.70353 | 0.8221 |
| p.rin | 0.2333 | 0.17789 | 0.2965 |
| p.tpdn | 0.0143 | 0.00173 | 0.0507 |
| p.tndp | 0.3286 | 0.22095 | 0.4512 |
| p.dntp | 0.0408 | 0.00498 | 0.1398 |
| p.dptn | 0.1429 | 0.09276 | 0.2066 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 47 | 2 | 49 |
| Test - | 23 | 138 | 161 |
| Total | 70 | 140 | 210 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.881 | 0.829 | 0.921 |
| 3 | se | 0.671 | 0.549 | 0.779 |
| 4 | sp | 0.986 | 0.949 | 0.998 |
| 6 | diag.or | 141.000 | 32.022 | 620.854 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 133 | 7 |
| 1 | 12 | 58 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.3095 | 0.2477 | 0.377 |
| tp | 0.3333 | 0.2700 | 0.401 |
| se | 0.8286 | 0.7197 | 0.908 |
| sp | 0.9500 | 0.8997 | 0.980 |
| diag.ac | 0.9095 | 0.8623 | 0.945 |
| diag.or | 91.8333 | 34.4026 | 245.137 |
| nndx | 1.2844 | 1.1264 | 1.615 |
| youden | 0.7786 | 0.6194 | 0.888 |
| pv.pos | 0.8923 | 0.7906 | 0.956 |
| pv.neg | 0.9172 | 0.8599 | 0.957 |
| lr.pos | 16.5714 | 7.9870 | 34.382 |
| lr.neg | 0.1805 | 0.1077 | 0.302 |
| p.rout | 0.6905 | 0.6232 | 0.752 |
| p.rin | 0.3095 | 0.2477 | 0.377 |
| p.tpdn | 0.0500 | 0.0203 | 0.100 |
| p.tndp | 0.1714 | 0.0918 | 0.280 |
| p.dntp | 0.1077 | 0.0444 | 0.209 |
| p.dptn | 0.0828 | 0.0435 | 0.140 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 58 | 7 | 65 |
| Test - | 12 | 133 | 145 |
| Total | 70 | 140 | 210 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.910 | 0.862 | 0.945 |
| 3 | se | 0.829 | 0.720 | 0.908 |
| 4 | sp | 0.950 | 0.900 | 0.980 |
| 6 | diag.or | 91.833 | 34.403 | 245.137 |
par(op)